Lossless compression of VQ index with search-order coding

Two-Layer Reversible Data Hiding for VQ-Compressed Images Based on De-Clustering and Indicator-Free Search-Order Coding

During transmission of digital images, secret messages can be embedded using data hiding techniques. Such techniques can transfer private secrets without drawing the attention of eavesdroppers. To reduce the amount of transmitted data, image compression methods are widely applied. Hiding secret data in compressed images is a hot issue recently. In this paper, we apply the de-clustering concept and the indicator-free search-order coding (IFSOC) technique to hide information into vector quantization (VQ) compressed images. Experimental results show that the proposed two-layer reversible data hiding scheme for IFSOC-encoded VQ index table can hide a large amount of secret data among state-of-the-art methods with a relatively lower bit rate and high security.

A novel joint data-hiding and compression scheme based on SMVQ and image inpainting

In this paper, we propose a novel joint data-hiding and compression scheme for digital images using side match vector quantization (SMVQ) and image inpainting. The two functions of data hiding and image compression can be integrated into one single module seamlessly. On the sender side, except for the blocks in the leftmost and topmost of the image, each of the other residual blocks in raster-scanning order can be embedded with secret data and compressed simultaneously by SMVQ or image inpainting adaptively according to the current embedding bit. Vector quantization is also utilized for some complex blocks to control the visual distortion and error diffusion caused by the progressive compression. After segmenting the image compressed codes into a series of sections by the indicator bits, the receiver can achieve the extraction of secret bits and image decompression successfully according to the index values in the segmented sections. Experimental results demonstrate the effectiveness of the proposed scheme.

HIDING IMAGES USING MODIFIED SEARCH-ORDER CODING AND MODULUS FUNCTION

This paper proposes a method for hiding an important image in a cover image whose size is limited. In this method, in order to save space, a modified search-order coding (MSOC) technique first transforms the important image, then, a randomization procedure permutes the transformed image to further increase the security. Finally, a modulus function embeds the permuted code in a cover image; notably, in the modules function, the modulus base used for a pixel is determined according to the variance of its neighboring pixels. Experimental results show that the images are of high quality. Comparisons with reported methods are provided.

Fast-searching algorithm for vector quantization using projection and triangular inequality

In this paper, a new and fast-searching algorithm for vector quantization is presented. Two inequalities, one used for terminating the searching process and the other used to delete impossible codewords, are presented to reduce the distortion computations. Our algorithm makes use of a vector's features (mean value, edge strength, and texture strength) to reject many unlikely codewords that cannot be rejected by other available approaches. Experimental results show that our algorithm is superior to other algorithms in terms of computing time and the number of distortion calculations. Compared with available approaches, our method can reduce the computing time and the number of distortion computations significantly. Compared with the best method of reducing distortion computation, our algorithm can further reduce the number of distortion calculations by 29% to 58.4%. Compared with the best encoding algorithm for vector quantization, our approach also further reduces the computing time by 8% to 47.7%.

An efficient prediction algorithm for image vector quantization

In this paper, we propose a novel efficient fuzzy prediction algorithm for image vector quantization by using the fact that the pixel values of a block is highly related to those of its adjacent blocks in the same image frame because of its spatial correlation. The experimental results show that our method performs better than other VQ algorithms, such as SOC-VQ, LCIC-VQ, STC-VQ, and DST-VQ, in terms of computational complexity and coding efficiency.

Adaptive vector quantization with codebook updating based on locality and history

In this paper, we propose two techniques that are applicable to any adaptive vector quantization (AVQ) systems. The first one is called the locality-based codebook updating: when performing a codebook updating, we update the operational codebook using not only the current input vector but also the codewords at all positions within a selected neighboring area (called the locality), while the operational codebook is organized in a "cache" manner. This technique is rationalized by the high correlation cross neighboring vectors that facilitates a more efficient coding of the indices of the codewords chosen from the codebook. The second technique is called the history aid, which makes use of the information of previously coded vectors to quantize the current input vector if it is used to update the operational codebook. A more effective AVQ system is obtained by combining together the history aid and the locality-based updating. Extensive simulations are carried out to demonstrate the improved results achieved by our AVQ systems. Particularly, when the operational codebook size is relatively small, the improvement over a benchmark AVQ system--the generalized threshold replenishment (GTR)--is drastic. For example, when the size is 32, testing on a nonstationary signal (containing frames from different video sequences, ordered in the concatenating or interleaving format) shows that the combination of history aid and locality-based updating offers more than 4 dB gain over GTR at 0.5 bpp.

A new dynamic finite-state vector quantization algorithm for image compression

The picture quality of conventional memory vector quantization techniques is limited by their supercodebooks. This paper presents a new dynamic finite-state vector quantization (DFSVQ) algorithm which provides better quality than the best quality that the supercodebook can offer. The new DFSVQ exploits the global interblock correlation of image blocks instead of local correlation in conventional DFSVQs. For an input block, we search the closest block from the previously encoded data using the side-match technique. The closest block is then used as the prediction of the input block, or used to generate a dynamic codebook. The input block is encoded by the closest block, dynamic codebook or supercodebook. Searching for the closest block from the previously encoded data is equivalent to expand the codevector space; thus the picture quality achieved is not limited by the supercodebook. Experimental results reveal that the new DFSVQ reduces bit rate significantly and provides better visual quality, as compared to the basic VQ and other DFSVQs.